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May 25, 2026
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
May 25, 2026
'The Humanities Serve as a Conscience'
Maria Mizernaia studies Soviet literature and the history of book publishing. In this interview for the HSE Young Scientists project, she discusses plans to publish a novel about besieged Leningrad, AI-provoked reflections on what it means to be human, and how novels can help satisfy our dopamine hunger.
May 25, 2026
Is It Possible to Predict a Citys Life Based on the Shape of Its Neighbourhoods?
Is it possible to predict, based on the configuration of streets and buildings, where a café will open or where traffic congestion will occur? Participants in the Spatial Analysis and Modelling of Urban Processes research and study group use open data and machine learning to identify universal patterns. Alexander Sheludkov and Eduard Somov discuss the purpose of comparing cities, the need for new forms of urban statistics, and how open data is transforming approaches to urban studies.

 

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Forward and Reverse Coding for Chromosome Transfer in Bacterial Nanonetworks

Nano Communication Networks. 2014. Vol. 5. No. 1-2. P. 15–24.
Petrov V., Balasubramaniam S., Lale R., Moltchanov D., Lio' P., Koucheryavy E.

Bacteria has been proposed in recent years as one approach to achieve molecular communication. Bacterial cells can harbour DNA encoded information and can deliver this information from one nanomachine to another by swimming (motility). One aspect of bacterial communication that could further enhance the performance of information delivery in bacterial nanonetworks is conjugation. Conjugation involves forming a physical connection between the bacteria in order to transfer DNA molecules (i.e., plasmids or chromosomes). However, the fragile physical connection between the bacteria is prone to breakage, in particular under mechanical stress. In this paper, a simple Forward and Reverse coding process is proposed to enhance the performance of information delivery in bacterial nanonetworks. The coding process involves segmenting messages into blocks and integrating this into the bacterial chromosome. Simulation work have been conducted to validate the efficiency of the coding process, where the results have shown positive performance compared to approaches that do not utilize coding or pure conjugation.

Language: English
Full text
Keywords: biological systemstelecommunicationsAlgorithmic information theory
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